With the advent of automation, humans’ role has become to do what computers cannot. Many more white-collar workers—perhaps all of them—will end up “working with data” to some extent. Digital transformations aim to use data to provide insights and guidance that computers alone cannot. Interpreting data insights requires “data literacy”; skills which are doubly important for anyone working alongside, supporting, leading, or hiring a data science and analytics team. Critical thinking skills for reading data insights are becoming core skills for 21st century workplaces. The course covers how to the ability to think rigorously and abstractly about evidence-based decision-making and manipulate data accordingly. It introduces a range of skills and applications related to critical thinking in such areas as forecasting, population measurement, set theory and logic, causal impact and attribution, scientific reasoning and the danger of cognitive biases. There are no prerequisites beyond high-school mathematics; this course has been designed to be approachable for everyone.
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Our leading course has transformed the machine-learning (ML), artificial intelligence (AI) and data science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data analytics and data science practitioners who have attended it. This course is an intuitive, hands-on introduction to AI, data science and machine learning. This is your artificial intelligence 101, data science 101 and machine learning 101 as there is significant overlap. The training focuses on central concepts and key skills, leaving you with a deep understanding of the foundations of AI and data science and even some of the more advanced tools used in the field. The skills taught are transferable to all software platforms, and the course does not involve coding, or require any coding knowledge or experience. A tool with a graphical user interface is used so you can focus on learning the central skills and ideas. Key skills taught include building, assessing, selecting and deploying predictive models, as well as employing some of the most commonly used methods in the field, including general linear models (GLMs), and advanced methods such as random forests. This also makes it your predictive analytics 101. The course also covers key issues of data science practice in a work environment, and directs you to a range of further learning directions.